3D/2D rodent brain extraction using shape model and instance learning
Accurate rodent brain extraction is one of the basic steps for many translational study using Magnetic Resonance Imaging (MRI). In this report, we present a new approach to model the rodent brain variation using non-rigid B-spline image registration for the brain extraction in MRI images. We mode...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/72200 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-72200 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-722002023-07-07T16:35:37Z 3D/2D rodent brain extraction using shape model and instance learning Ling, Chen Lin Zhiping School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Accurate rodent brain extraction is one of the basic steps for many translational study using Magnetic Resonance Imaging (MRI). In this report, we present a new approach to model the rodent brain variation using non-rigid B-spline image registration for the brain extraction in MRI images. We model the shape and appearance with the B-spline parameters together with a mean brain image. Followed by a method using multi-expert, we refine the brain extraction region. Compared with the image-based template model using cross-correlation, the performance for rodent brain extraction has shown much improvement on one data set while maintaining the similar yet more consistent performance for another. Both template based methods however outperform the voxel based method (3D PCNN) and a modified BET version for rodent brain extraction. Bachelor of Engineering 2017-05-29T09:02:29Z 2017-05-29T09:02:29Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/72200 en Nanyang Technological University 48 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Electrical and electronic engineering |
spellingShingle |
DRNTU::Engineering::Electrical and electronic engineering Ling, Chen 3D/2D rodent brain extraction using shape model and instance learning |
description |
Accurate rodent brain extraction is one of the basic steps for many translational study using
Magnetic Resonance Imaging (MRI). In this report, we present a new approach to model the
rodent brain variation using non-rigid B-spline image registration for the brain extraction in
MRI images. We model the shape and appearance with the B-spline parameters together with
a mean brain image. Followed by a method using multi-expert, we refine the brain extraction
region. Compared with the image-based template model using cross-correlation, the
performance for rodent brain extraction has shown much improvement on one data set while
maintaining the similar yet more consistent performance for another. Both template based
methods however outperform the voxel based method (3D PCNN) and a modified BET version
for rodent brain extraction. |
author2 |
Lin Zhiping |
author_facet |
Lin Zhiping Ling, Chen |
format |
Final Year Project |
author |
Ling, Chen |
author_sort |
Ling, Chen |
title |
3D/2D rodent brain extraction using shape model and instance learning |
title_short |
3D/2D rodent brain extraction using shape model and instance learning |
title_full |
3D/2D rodent brain extraction using shape model and instance learning |
title_fullStr |
3D/2D rodent brain extraction using shape model and instance learning |
title_full_unstemmed |
3D/2D rodent brain extraction using shape model and instance learning |
title_sort |
3d/2d rodent brain extraction using shape model and instance learning |
publishDate |
2017 |
url |
http://hdl.handle.net/10356/72200 |
_version_ |
1772825702509314048 |